Do heavily shorted stocks squeeze higher?
Not in the data. Since 2021, US stocks whose short positions needed 10 or more days of normal volume to cover beat the same day's median stock over the next month 49 times in 100, and over three months 49; a coin flip gives 50. Ranking by days to cover showed no measurable edge either. Squeezes happen in single stocks; the typical heavily shorted stock did not beat the median.
The paper
The full study: its data, method, robustness, limits and sources.
Opulence Alpha Research · Published Sep 25, 2026 · Data through Aug 21, 2026
Keywords: short squeeze, days to cover, high short interest stocks, short squeeze stocks, does high short interest predict a squeeze, short interest ratio·JEL classification: G11, G12, G14, C12, C58
- Universe
- The same for every study: 1,767 US common stocks in the 11 GICS sectors, 422 of them since delisted; S&P 500 members since 1996 plus large and mid-sized companies outside the index
- Period
- Every Wednesday from Jan 4, 1995 to Aug 19, 2026: 1,634 Wednesdays across 7,959 trading sessions
- Sample
- n = 1,763 stocks of the 1,767-stock universe; 1,889,758 stock-weeks, a median of 1,148 stocks per Wednesday
- Outcome
- Return over the next 1, 5, 21 and 63 trading sessions against the same day's median stock
- Inference
- t on non-overlapping dates; proven only when |t| ≥ 3 and the same sign in at least 60% of years
1Introduction
Short interest is the number of a company's shares borrowed, shorted and not yet returned. When it is large against normal trading volume, short sellers need many days to close their positions, and a rising price can force them to hurry, pushing it higher still: the short squeeze. Ten or more days to cover is a common screen for such setups on forums and in the financial press. Academic studies have more often found the reverse tendency, heavily shorted stocks trailing on average (Asquith, Pathak and Ritter 2005; Boehmer, Jones and Zhang 2008). This study asks whether, in recent US data, stocks with high days to cover went on to beat other stocks.
2Data and method
2.1Sample design
Population and frame. The population is US common stocks listed on the NYSE and Nasdaq; funds, ETFs, trusts, preferred shares, warrants and units are excluded. The sampling frame is a fixed universe of 1,767 companies, drawn once when the platform was built and not re-sampled since, in two strata. Stratum 1 is a census of the S&P 500: every company in the index at any time since 1996 whose price history could be recovered, 1,027 companies of which 413 have since delisted; it holds 76% of the index's members in 1996 and at least 96% in every year from 2010. Stratum 2 is 740 large and mid-sized companies outside the index, selected in proportion to the market's sector weights from the stocks that passed a minimum share price of $15 and a minimum average daily trading value of $25 million; 9 of them have since delisted.
Sample. The unit of observation is a stock-week: one stock on one Wednesday. A stock enters a Wednesday's cross-section when it has a valid close that day, a value of the signal and a measured outcome; bars flagged as bad data and returns that cross a change of issuer are left out. This study's sample is n = 1,763 stocks of the 1,767: 1,889,758 stock-weeks on 1,634 Wednesdays from Jan 4, 1995 to Aug 19, 2026, a median of 1,148 stocks per Wednesday (range 820 to 1,371). 331,954 stock-weeks (17.6%) come from the 422 companies that have since delisted. Every stock and every Wednesday carries equal weight.
Representativeness. Table 1 gives the sample by GICS sector beside the S&P Composite 1500: 7.9% of companies would have to change sector for the two to match exactly. By latest market value, 55% of the active companies are large (at least $10bn), 38% mid ($2–10bn) and 6% small. Because Stratum 2 was chosen from companies listed at construction, its history carries survivorship bias. Section 4 repeats the lead result among the stocks that were S&P 500 members on each date, the part of the sample largely free of that bias.
Companies in this study's sample, of them those since delisted, the size of the active companies, the sample's share of stock-weeks and the sector's share of the S&P Composite 1500.
| Sector | Sample | Delisted | Large | Mid | Small | Stock-weeks | S&P 1500 |
|---|---|---|---|---|---|---|---|
| Information Technology | 282 | 67 | 122 | 76 | 18 | 13.9% | 12.7% |
| Financials | 256 | 60 | 114 | 74 | 8 | 14.6% | 17.2% |
| Industrials | 255 | 53 | 126 | 67 | 11 | 15.9% | 17.5% |
| Health Care | 237 | 47 | 92 | 81 | 16 | 12.4% | 10.9% |
| Consumer Discretionary | 209 | 39 | 69 | 87 | 12 | 12.3% | 12.9% |
| Energy | 105 | 30 | 39 | 31 | 5 | 5.9% | 4.7% |
| Consumer Staples | 101 | 38 | 40 | 17 | 5 | 5.9% | 4.9% |
| Materials | 94 | 34 | 35 | 24 | 2 | 5.6% | 5.1% |
| Real Estate | 84 | 10 | 40 | 32 | 2 | 5.7% | 6.9% |
| Communication Services | 80 | 31 | 28 | 13 | 5 | 3.9% | 3.3% |
| Utilities | 60 | 13 | 35 | 12 | 0 | 4% | 4% |
| All sectors | 1,763 | 422 | 740 | 514 | 84 | 100% | 100% |
Sample: n = 1,763 of the 1,767 companies. Size by latest market value for the 1,338 active companies with one: large ≥ $10bn, mid $2–10bn, small < $2bn. S&P 1500 shares count the constituents of the S&P 500, MidCap 400 and SmallCap 600 (1,506 companies, lists read Sep 25, 2026). Sectors are each company's current GICS sector.
2.2Signals
| Signal | Definition | Since | Wednesdays |
|---|---|---|---|
| 10 or more days to cover | Shares held short divided by average daily volume, from the latest published short-interest report, at 10 or more | Jun 30, 2021 | 265 |
| Days to cover | Shares held short divided by average daily volume, from the latest published short-interest report | Jun 30, 2021 | 266 |
2.3Measurement
Outcome. The return from the close on the Wednesday to the close 1, 5, 21 and 63 trading sessions later, on closes adjusted for splits and dividends, compared with the same day's median stock; half of all stocks beat the median by construction, so chance is 50 in 100 on every date. No delisting return is added.
Each Wednesday, the stocks showing the reading are scored against the same day's median stock. A state counts on every Wednesday it holds; a cross or breakout counts only on the Wednesday it happens.
Each Wednesday the stocks are ranked on the signal. The study reports how often each fifth of that ranking beat the median stock, and the rank correlation (IC) between the signal and the return that followed.
The t-statistic uses non-overlapping dates only. A result is called proven when |t| is at least 3, it held in at least 60% of years and it is large enough to matter; with 1,470 tests across the studies, a looser bar would pass dozens by luck.
Full data and methods3Results
Figure 1 reads every horizon for stocks with 10 or more days to cover. They beat the median stock 49 times in 100 over the next session, 49 over the next week, 49 over the next month and 49 over the next three months. All four readings sit a little below a coin flip; the next-session one is faint, the other three show no measurable edge. None leans the way the squeeze story says.
Share of stocks showing the reading that beat the same day's median stock, by horizon. The line at 50 is chance, the grey band the range chance alone produces; a filled square is a proven relation.
Every Wednesday the stocks are cut into fifths on the signal, lowest to highest; bars show how often each fifth beat the same day's median stock over the next month. The dashed line is chance.
Next month.
Next week, three-session return against the industry; the same way in 32 of 32 years.
Rank correlation between the signal and the return that followed, with its 95% interval. An interval that crosses zero is no relation.
Each square is one calendar year; filled = a year in which the average stock showing the signal lagged the median stock over the next month. Years are counted, not shown in order.
A signal with an edge would fill most squares, or leave most empty.
Rank correlation inside each GICS sector over the next month, stocks ranked only against their own sector. Colour only where |t| ≥ 2.
Out of every 100 stocks with 10 or more days to cover, 49 beat the median stock over the following month (Figure 2). Ranked into fifths by days to cover, the fifth with the most beat the median stock 49 times in 100 over the next month and the fifth with the fewest 50 (Figure 3); the rank correlation was -0.010, and it stays close to zero at every horizon (Figure 4). Their average returns against the average stock were 0.19 and 0.11 points; an average counts the size of each move, while the share counts only how often a stock came out ahead.
4Robustness
Table 3 reads the 10-day zone horizon by horizon: over the next month it lagged the median stock in 4 of 6 years. The squeeze score orders stocks exactly as days to cover does, so its rows in Table 4 repeat those for days to cover. The strongest reading there is among S&P 500 members on the date, and it leans against the squeeze story: higher days to cover, a slightly weaker month (IC -0.023, t -3.1). It is one subset of a short record, not a result on its own.
Share that beat the median stock, its t-statistic on non-overlapping dates, and the years in which it pointed the same way.
| Horizon | Beat the median | t | Years, same way |
|---|---|---|---|
| 10 or more days to cover, session | 48.8 | -2.1 | 5 / 6 |
| 10 or more days to cover, week | 49.3 | -1.0 | 4 / 6 |
| 10 or more days to cover, month | 48.5 | -0.4 | 4 / 6 |
| 10 or more days to cover, three months | 48.9 | 0.1 | 4 / 6 |
Rank IC and its t-statistic on non-overlapping dates.
| Sample | IC | t | Wednesdays |
|---|---|---|---|
| Squeeze score, next month | |||
| All stocks, whole period | -0.0102 | -0.9 | 262 |
| By decade: 1995–2004 | |||
| By decade: 2005–2014 | |||
| By decade: 2015– | -0.0102 | -0.9 | 262 |
| S&P 500 members on the date | -0.0233 | -3.1 | 262 |
| Sectors with the overall sign | 7 / 11 | ||
| Days to cover, next month | |||
| All stocks, whole period | -0.0102 | -0.9 | 262 |
| By decade: 1995–2004 | |||
| By decade: 2005–2014 | |||
| By decade: 2015– | -0.0102 | -0.9 | 262 |
| S&P 500 members on the date | -0.0233 | -3.1 | 262 |
| Sectors with the overall sign | 7 / 11 |
5Limitations
- The short-interest record in this data starts in 2021, after that year's best-known squeezes, and covers 6 calendar years. The universe's earlier decades carry no short-interest reading, and a longer record could read differently.
- Short interest is reported twice a month and published days after the date it describes. Each reading is dated by its publication, as a reader would see it, so a squeeze already under way by then is partly in the past.
- The study counts how often heavily shorted stocks beat the median stock, not the size of the rare, sharp rises a squeeze produces; a few large squeezes can lift a group's average return while most of its members trail.
- Before costs. Averages exclude trading costs, taxes and market impact.
- Same-close timing. Returns start at the close the signal is computed from; a real trade would start later.
- Survivors among smaller companies. The non-index names were chosen from companies listed when the universe was built; no delisting returns are added.
6Conclusion
Days to cover measures how crowded the exit is for short sellers: who is betting against a stock, and how long they would take to leave. Measured against the same day's median stock since 2021, stocks with 10 or more days to cover did not go on to beat other stocks, and ranking by days to cover showed no measurable edge in either direction. A squeeze is a story told about a single stock after the fact; across hundreds of stocks, the typical one with a high days-to-cover reading did not go on to beat the median stock.
References
- Asquith, P., Pathak, P. A. and Ritter, J. R. (2005). Short Interest, Institutional Ownership, and Stock Returns. Journal of Financial Economics 78(2).
- Boehmer, E., Jones, C. M. and Zhang, X. (2008). Which Shorts Are Informed? Journal of Finance 63(2).
- Benjamini, Y. and Hochberg, Y. (1995). Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing. Journal of the Royal Statistical Society, Series B 57(1).
- Grinold, R. C. and Kahn, R. N. (2000). Active Portfolio Management, 2nd ed. McGraw-Hill.
- Harvey, C. R., Liu, Y. and Zhu, H. (2016). … and the Cross-Section of Expected Returns. Review of Financial Studies 29(1).
- Shumway, T. (1997). The Delisting Bias in CRSP Data. Journal of Finance 52(1).
Appendix A. Questions readers ask
- Do stocks with high short interest squeeze higher?
- Not as a rule. Since 2021, US stocks with 10 or more days to cover beat the same day's median stock over the next month 49 times in 100, against 50 for a coin flip. Squeezes do happen in single stocks, but the typical heavily shorted stock did not beat the median; the study does not measure the size of the rare squeezes themselves.
- What is days to cover?
- Days to cover divides the shares held short by the stock's average daily volume: roughly how many days of normal trading short sellers would need to close their positions. A reading of 10 or more is often quoted as a squeeze setup.
- Is a high squeeze score a better signal?
- Not in this data. The squeeze score used here multiplies days to cover by its rank among all stocks, so it orders stocks exactly as days to cover does and gives the same result: no measurable edge.
- How was the short squeeze setup measured?
- Every Wednesday from 2021, when the short-interest record begins, to Aug 19, 2026, stocks were compared with the same day's median stock over the next 1, 5, 21 and 63 trading sessions, on the same universe of 1,767 US stocks every study uses. Each short-interest reading is dated by the day it was published.
Data availability and citation
Every figure in this paper is quoted from one frozen snapshot, published as JSON with the sample description, the robustness results and the test counts. The same snapshot feeds the research console, so the two cannot disagree.
Opulence Alpha Research (2026). Do heavily shorted stocks squeeze higher? Opulence Alpha Studies, Sep 25, 2026. https://opulencealpha.ai/studies/short-squeeze
Research, not advice. A measured tendency across hundreds of stocks is a nudge for any one of them, never a forecast of its price.